library(cloudml)
gcloud_install()
job <- cloudml_train("train.R") cloudml_train
Train a model using Cloud ML
Description
Upload a TensorFlow application to Google Cloud, and use that application to train a model.
Usage
cloudml_train(file = "train.R", master_type = NULL, flags = NULL,
region = NULL, config = NULL, collect = "ask", dry_run = FALSE) Arguments
| Arguments | Description |
|---|---|
| file | File to be used as entrypoint for training. |
| master_type | Training master node machine type. “standard” provides a basic machine configuration suitable for training simple models with small to moderate datasets. See the documentation at https://cloud.google.com/ml-engine/docs/tensorflow/machine-types#machine_type_table for details on available machine types. |
| flags | Named list with flag values (see flags()) or path to YAML file containing flag values. |
| region | The region to be used for training. |
| config | A list, YAML or JSON configuration file as described https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs. |
| collect | Logical. If TRUE, collect job when training is completed (blocks waiting for the job to complete). The default ("ask") will interactively prompt the user whether to collect the results or not. |
| dry_run | Triggers a local dry run over the deployment phase to validate packages and packing work as expected. |
Examples
See Also
job_status(), job_collect(), job_cancel() Other CloudML functions: cloudml_deploy, cloudml_predict